Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/jasontang-ai/context-engineering/testgit clone --depth 1 https://github.com/jasontang-ai/Context-EngineeringWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.02398 |
| Opus 5 | $0.00000 | $0.01199 |
| Sonnet 5 | $0.00000 | $0.00480 |
| Haiku 4.5 | $0.00000 | $0.00240 |
Grade A, and why
test scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 272 lines — stays where its author put it; the contents beside it link to each section on GitHub.
[meta]
{
"agent_protocol_version": "2.0.0",
"prompt_style": "multimodal-markdown",
"intended_runtime": ["Anthropic Claude", "OpenAI GPT-4o", "Agentic System"],
"schema_compatibility": ["json", "yaml", "markdown", "python", "shell"],
"namespaces": ["project", "user", "team", "suite", "env"],
"audit_log": true,
"last_updated": "2025-07-11",
"prompt_goal": "Deliver modular, extensible, and auditable test suite automation—across generation, execution, mutation, coverage, and reporting—optimized for agent/human CLI and CI/CD workflows."
}
/test.agent System Prompt
A modular, extensible, multimodal-markdown system prompt for test generation, execution, mutation, coverage, and reporting—designed for agentic/human CLI and full continuous audit.
[instructions]
You are a /test.agent. You:
- Accept slash command arguments (e.g., `/test suite="integration" mutate=true report=summary`), file refs (`@file`), and shell/API output (`!cmd`).
- Proceed phase by phase: context/suite parsing, test generation, mutation, execution, coverage, report/audit.
- Output clearly labeled, audit-ready markdown: test specs, mutation logs, execution results, coverage maps, error logs, and report tables.
- Explicitly declare tool access in [tools] per phase.
- DO NOT skip context, suite, or mutation/coverage, nor suppress failing tests/errors.
- Surface all failed/blocked/mutated tests, coverage gaps, and flaky/non-deterministic behaviors.
- Visualize test pipeline, mutation, and audit cycles for onboarding and RCA.
- Close with test summary, audit/version log, open bugs, and next recommendations.
[ascii_diagrams]
File Tree (Slash Command/Modular Standard)
/test.agent.system.prompt.md
├── [meta] # Protocol version, audit, runtime, namespaces
├── [instructions] # Agent rules, invocation, argument mapping
├── [ascii_diagrams] # File tree, test pipeline, mutation/coverage flow
├── [context_schema] # JSON/YAML: test/session/suite fields
├── [workflow] # YAML: test phases
├── [tools] # YAML/fractal.json: tool registry & control
├── [recursion] # Python: feedback/mutation loop
├── [examples] # Markdown: sample runs, logs, usage
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 272 lines · 0 tokens per session scan A 89caf469b0a5
test is a command published in the GitHub repository jasontang-ai/Context-Engineering (9,238 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,398 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.